1. Field of the Invention
The present invention relates to universal pattern processing tasks.
2. Description of the Related Art
Universal pattern processing tasks include at least any process that involves pattern recognition tasks (e.g speech or image recognition), segmentation (e.g. audio segmentation), identification (e.g. speaker), verification (e.g. speaker), search, translation, text-to-speech, information retrieval, audio-retrieval, image retrieval, language processing tasks, summarization, simplification etc.
In general, in universal pattern processing tasks such as classification, segmentation, translation, search etc., the best model is identified as follows. Let F(Y,λ) be some score (e.g. likelihood score) function that characterizes a model λ given data Y. Then the “best” model could be found by the following rule:{tilde over (λ)}=argmaxλεΘF(Y,λ)   (1)where Θ is a family of models.
That is, the quality of the model was measured by how well the model fit the data. A likelihood score is calculated between the model and the data, and if the likelihood score is high, then the model fits the data well. To have a function such as matching maximum likelihood to match a model to data, the function and data must be trained. The likelihood score measured how well the data fit the model.
This method in most cases does not give perfect processing recognition accuracy. Thus, a need exists for additional methods to improve pattern processing in various tasks.